Closing the Loop for Edge Detection and Object Proposals

Abstract

Edge grouping and object perception are unified procedures in perceptual organization. However the computer vision literature classifies them as independent tasks. In this paper, we argue that edge detection and object proposals should benefit one another. To achieve this, we go beyond bounding boxes and extract closed contours that represent potential objects within. A novel objectness metric is proposed to score and rank the proposal boxes by considering the sizes and edge intensities of the closed contours. To improve the edge detector given the top-down object proposals, we group local closed contours and construct global object hierarchies and segmentations. The edge detector is retrained and enhanced using these hierarchical segmentations as additional feature channels. In the experiments we show that by closing the loop for edge detection and object proposals, we observe improvements for both tasks. Unifying edges and object proposals is valid and useful.